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Using the Alzheimer's Disease Neuroimaging Initiative dataset, the authors identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. They trained convolutional neural networks to predict standardized uptake value ratio and classify amyloid status. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-squared error of 0.054, corresponding to 95.1% correct amyloid status prediction. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively). They conclude that deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans and have promise to automate this laborious calculation.

With prospective imaging to screen the spinal cord, the authors found SCCMs in 21 of 29 familial CCM1 patients, a prevalence of 72.4%. They conclude that the study demonstrates that SCCMs are indeed a common finding in patients with familial CCM and supports the idea of familial CCM syndrome as a progressive systemic disease that affects the entire central nervous system. They found an expected positive correlation of number of SCCMs with both patient age and number of intracranial CCMs. They also found a high prevalence of vertebral intraosseous vascular malformations (69%), including atypical (T1 hypointense) intraosseous vascular malformation in approximately 38% of the patients who underwent MR imaging screening.